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Found 6 papers, 3 papers with code
Communication Efficient Private Federated Learning Using Dithering
The task of preserving privacy while ensuring efficient communication is a fundamental challenge in federated learning.
Privacy Amplification via Random Participation in Federated Learning
In this work, in a federated setting, we consider random participation of the clients in addition to subsampling their local datasets.
Over-the-Air Ensemble Inference with Model Privacy
We consider distributed inference at the wireless edge, where multiple clients with an ensemble of models, each trained independently on a local dataset, are queried in parallel to make an accurate decision on a new sample.
Speeding Up Private Distributed Matrix Multiplication via Bivariate Polynomial Codes
We consider the problem of private distributed matrix multiplication under limited resources.
Information Theory Cryptography and Security Distributed, Parallel, and Cluster Computing Information Theory
Dopamine: Differentially Private Federated Learning on Medical Data
While rich medical datasets are hosted in hospitals distributed across the world, concerns on patients' privacy is a barrier against using such data to train deep neural networks (DNNs) for medical diagnostics.
Private Wireless Federated Learning with Anonymous Over-the-Air Computation
In conventional federated learning (FL), differential privacy (DP) guarantees can be obtained by injecting additional noise to local model updates before transmitting to the parameter server (PS).
